# Named Entity Recognition Based Automatic Generation of Research Highlights

arXiv: https://arxiv.org/abs/2303.12795 · HTML: https://mlsanity.com/paper/2303.12795/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 27 references that are in the map.
- First posted on arXiv 25 Feb 2023.

## Builds on (most cited first, 12 of 12)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Get To The Point: Summarization with Pointer-Generator Networks](https://mlsanity.com/paper/1704.04368/) (2017)
- [PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization](https://mlsanity.com/paper/1912.08777/) (2019)
- [Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond](https://mlsanity.com/paper/1602.06023/) (2016)
- [A Neural Attention Model for Abstractive Sentence Summarization](https://mlsanity.com/paper/1509.00685/) (2015)
- [Modeling Coverage for Neural Machine Translation](https://mlsanity.com/paper/1601.04811/) (2016)
- [Growth rates of modern science: A latent piecewise growth curve approach to model publication numbers from established and new literature databases](https://mlsanity.com/paper/2012.07675/) (2020)
- [A Supervised Approach to Extractive Summarisation of Scientific Papers](https://mlsanity.com/paper/1706.03946/) (2017)
- [Towards Abstraction from Extraction: Multiple Timescale Gated Recurrent Unit for Summarization](https://mlsanity.com/paper/1607.00718/) (2016)
- [Text Summarization of Czech News Articles Using Named Entities](https://mlsanity.com/paper/2104.10454/) (2021)

## Built on by (most cited first, 10 of 10)

- [From Paper to Card: Transforming Design Implications with Generative AI](https://mlsanity.com/paper/2403.08137/) (2024)
- [Hallucination Reduction in Long Input Text Summarization](https://mlsanity.com/paper/2309.16781/) (2023)
- [Can pre-trained language models generate titles for research papers?](https://mlsanity.com/paper/2409.14602/) (2024)
- [Evaluating LLMs and Pre-trained Models for Text Summarization Across Diverse Datasets](https://mlsanity.com/paper/2502.19339/) (2025)
- [Analysis of Multidomain Abstractive Summarization Using Salience Allocation](https://mlsanity.com/paper/2402.11955/) (2024)
- [How Green are Neural Language Models? Analyzing Energy Consumption in Text Summarization Fine-tuning](https://mlsanity.com/paper/2501.15398/) (2025)
- [Comparative Analysis of Abstractive Summarization Models for Clinical Radiology Reports](https://mlsanity.com/paper/2506.16247/) (2025)
- [Overview of the SciHigh Track at FIRE 2025: Research Highlight Generation from Scientific Papers](https://mlsanity.com/paper/2601.11582/) (2026)
- [Enhancing Unsupervised Keyword Extraction in Academic Papers through Integrating Highlights with Abstract](https://mlsanity.com/paper/2604.19505/) (2026)
- [Automatic Generation of Titles for Research Papers Using Language Models](https://mlsanity.com/paper/2606.05085/) (2026)

## Abstract (arXiv)

A scientific paper is traditionally prefaced by an abstract that summarizes the paper. Recently, research highlights that focus on the main findings of the paper have emerged as a complementary summary in addition to an abstract. However, highlights are not yet as common as abstracts, and are absent in many papers. In this paper, we aim to automatically generate research highlights using different sections of a research paper as input. We investigate whether the use of named entity recognition on the input improves the quality of the generated highlights. In particular, we have used two deep learning-based models: the first is a pointer-generator network, and the second augments the first model with coverage mechanism. We then augment each of the above models with named entity recognition features. The proposed method can be used to produce highlights for papers with missing highlights. Our experiments show that adding named entity information improves the performance of the deep learning-based summarizers in terms of ROUGE, METEOR and BERTScore measures.